23 citations · 38 across the 19 of their papers we have counts for
21 papers
ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models
Hyomin Kim, Bowen Chen, Jin Huang +3
Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such promotion decisions remains un…
Generative AI Advertising as a Problem of Trustworthy Commercial Intervention
Jingyi Qiu, Qiaozhu Mei
Major deployed generative AI advertising systems preserve a visible boundary between commercial content and AI-generated responses. Yet empirical research shows that ads woven dire…
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
Xingjian Zhang, Sophia Moylan, Ziyang Xiong +3
Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems.…
Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters
Xingjian Zhang, Tianhong Gao, Suliang Jin +4
Large language models (LLMs) are increasingly used as raters for evaluation tasks. However, their reliability is often limited for subjective tasks, when human judgments involve su…
Pathways of Thoughts: Multi-Directional Thinking for Long-form Personalized Question Answering
Alireza Salemi, Cheng Li, Mingyang Zhang +7
Personalization is well studied in search and recommendation, but personalized question answering remains underexplored due to challenges in inferring preferences from long, noisy,…
Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs
Alan Zhu, Jiaqi Ma, Qiaozhu Mei
As large graph datasets become increasingly common across many fields, sampling is often needed to reduce the graphs into manageable sizes. This procedure raises critical questions…
Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation
Alireza Salemi, Cheng Li, Mingyang Zhang +6
Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One wa…
Content Quality vs. Attention Allocation: An LLM-Based Case Study in Peer-to-peer Mental Health Networks
Teng Ye, Hanson Yan, Xuhuan Huang +4
With the rise of social media and peer-to-peer networks, users increasingly rely on crowdsourced responses for information and assistance. However, the mechanisms used to rank and…
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
Zhuowan Li, Cheng Li, Mingyang Zhang +2
Retrieval Augmented Generation (RAG) has been a powerful tool for Large Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini-1.5…
Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses
Teng Ye, Jingnan Zheng, Junhui Jin +3
While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those fr…
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows
Xingjian Zhang, Yutong Xie, Jin Huang +8
Scientific innovation relies on detailed workflows, which include critical steps such as analyzing literature, generating ideas, validating these ideas, interpreting results, and i…
Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke, Weize Kong, Cheng Li +3
Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performanc…
Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience
Tao Chen, Siqi Zuo, Cheng Li +3
In business and marketing, analyzing the reasons behind buying is a fundamental step towards understanding consumer behaviors, shaping business strategies, and predicting market ou…
PRewrite: Prompt Rewriting with Reinforcement Learning
Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang +2
Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffe…
A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?
Qiaozhu Mei, Yutong Xie, Walter Yuan +1
We administer a Turing Test to AI Chatbots. We examine how Chatbots behave in a suite of classic behavioral games that are designed to elicit characteristics such as trust, fairnes…
A Metadata-Driven Approach to Understand Graph Neural Networks
Ting Wei Li, Qiaozhu Mei, Jiaqi Ma
Graph Neural Networks (GNNs) have achieved remarkable success in various applications, but their performance can be sensitive to specific data properties of the graph datasets they…
Meta Semantic Template for Evaluation of Large Language Models
Yachuan Liu, Liang Chen, Jindong Wang +2
Do large language models (LLMs) genuinely understand the semantics of the language, or just memorize the training data? The recent concern on potential data contamination of LLMs h…
Automated Evaluation of Personalized Text Generation using Large Language Models
Yaqing Wang, Jiepu Jiang, Mingyang Zhang +4
Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context. While the research progress in this area has bee…
Learning to Rewrite Prompts for Personalized Text Generation
Cheng Li, Mingyang Zhang, Qiaozhu Mei +2
Facilitated by large language models (LLMs), personalized text generation has become a rapidly growing research direction. Most existing studies focus on designing specialized mode…
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei +1
Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies de…
Understanding Diffusion of Recurrent Innovations
Fuqi Lin
The diffusion of innovations theory has been studied for years. Previous research efforts mainly focus on key elements, adopter categories, and the process of innovation diffusion.…